1. Field
This disclosure relates to image processing in general and image enhancement in particular.
2. Background
Image processing is any form of signal processing for which the input is an image. For example, the input image may be a photograph, video, video frame, or digitally created image. The output of image processing may be another image, or parameters related to the image that may characterize the image. Many image processing techniques treat an image as a two-dimensional signal and apply signal-processing techniques to it. Image processing is used in a wide variety of scientific, engineering, and medical disciplines including photography, computer graphics, computer vision, photo analysis, pattern recognition, fingerprint analysis, imagery, facial recognition, analysis of structural and material damage and defects, and radar, as well as many others.
An important application of image processing is medical imaging. Medical imaging has played an increasing role in the detection and diagnosis of disease and medical anomalies over the past few decades. Imaging and image processing are used routinely in the analysis of X-ray diagnostics, ultrasound, and in three-dimensional visualization of computed tomography (CT), magnetic resonance imaging (MRI) data, and the like. The current state of the art is the result of significant advances in nearly all aspects of image processing including image segmentation, quantification, enhancement, visualization, compression, and storage.
Image enhancement refers to techniques that are used to adjust an image, including techniques to improve contrast and reduce noise. Image segmentation is used to identify structures of interest in an image and to differentiate them. Techniques used in image segmentation include thresholding, region growing, and pattern recognition, for example. Quantification is applied to segmented structures to extract important diagnostic information such as shape, size, texture, etc., of features in a medical image. Registration refers to the process of correctly registering (i.e., lining up) two images of the same subject/target that are obtained by different modalities such as from a CT scan and an MRI scan. Visualization refers to the use of specialized hardware and software to visually inspect medical and biological data. Contrast enhancement commonly refers to changing pixel values based on intensity curves. Compression, storage, and communication of medical images constitute a field for which there is increasing demand due to the large volume of data that can be produced in modern diagnostic tests.
Current image enhancement techniques can be divided into two categories: (1) spatial domain methods, and (2) frequency domain methods. Spatial domain methods manipulate the pixel intensity values to achieve a desired enhancement. Frequency domain methods usually involve performing a Fourier Transform on the image. Next, image enhancement manipulations are carried out on the Fourier Transformed image. Finally, an Inverse Fourier Transform is carried out to produce a final enhanced image.
Spatial domain image enhancement algorithms can be thought of as a transform s=T(r) of one pixel intensity r in the raw image to yield a new pixel intensity s in the enhanced image. In this context, the transform “T” is any function that transforms the intensity of a given pixel from the value “r” to the value “s.” The range of possible values for a grey-scale pixel is determined by the number of bits “k” used to represent the intensity. The range of intensity values falls in the interval {0, (L−1)} where L=2k. For example, for an 8-bit image (i.e., k=8), the range would be in the interval {0, 255}. Color images can be represented by associating with each pixel a plurality of intensities (e.g., three color intensities, one each for red, green, and blue). Other pixel intensity representations are common in the field as would be apparent to a person of ordinary skill in the relevant art.
When carrying out a transform in the spatial domain, it is often convenient to normalize the intensity values r and s, to lie in the range {0, 1}. The transformation s=log(1+r) is a simple example of a pixel intensity transform in the spatial domain.
There are no existing image enhancement algorithms that solve all of the technical challenges faced by the field of medical imaging and diagnosis. There is therefore a need for improved image enhancement techniques for medical anomaly and disease detection and diagnosis.